1 Introduction

This report examines the impact of major disruptions—including the COVID-19 pandemic, significant public events, and extreme weather—on train usage patterns in Sydney between 2020 and 2025. Using Opal card data and Bureau of Meteorology (BOM) records, we analyze changes in passenger volumes, identify key trends, and compare the magnitude and duration of different types of disruptions.

2 COVID-19 Impact on Sydney Train Usage

Below, we analyze the impact of COVID-19 on Sydney train station usage using Opal data. The code and analysis are directly adapted from T1.qmd, with each code block preceded by a description of its purpose.

2.1 Interactive Visual for Entry / Exits

This code generates a small bar chart for each station (as a tooltip), showing entries and exits by phase, and combines this with drop/recovery statistics for use in an interactive map.

2.2 Create interactive map of train station usage and drop/recovery

This code defines a function to plot all stations on a map, color-coded by route, with interactive tooltips showing drop and recovery statistics and a bar chart for each station. The map supports zooming.

2.3 Plot monthly entry trend for Central Station

This code creates an interactive time series plot of monthly entries at Central Station, highlighting the lowest and highest points.

2.4 Interactive time series for all stations with dropdown

This code creates an interactive time series plot for monthly entries at every station, with a dropdown menu to select the station.

3 Public Events and Temporary Increases in Ridership

Events, Festivals, and Spikes

Public events such as Vivid Sydney, New Year’s Eve, and major concerts have a significant but temporary impact on train ridership in Sydney. This section analyzes Opal card data to quantify changes in train usage during these events, with a focus on the Sydney CBD (Circular Quay, Martin Place, Town Hall).

3.1 Vivid Sydney: Impact on Train Usage

Vivid Sydney is an annual festival that draws large crowds to the CBD. We compare average train tap-ons during Vivid Sydney to normal days, both for all-day and evening (7–10pm) periods.

3.2 Major Concerts: Taylor Swift, The Weeknd, Coldplay

In 2024, several high-profile concerts were held in Sydney, including Taylor Swift (February), The Weeknd (October), and Coldplay (December). The following analysis compares average train tap-ons on concert days to normal days, for both all-day and evening periods.

3.3 Public Holidays and Year-End Shutdown

Public holidays and the annual year-end shutdown (December 25 to January 4) also affect train ridership patterns. This section compares average train tap-ons during these periods to normal days, using data from 2020 to 2024.

3.4 Comparative Impact of Events: Radar Chart

The following radar chart summarizes the average train tap-ons by period (Normal, Vivid Sydney, Public Holidays, Shutdown) for each year. This visualization provides a comparative overview of the impact of different event types on train ridership in the Sydney CBD.

Inference: Public events and holidays cause sharp but short-lived increases or decreases in train ridership, with the most pronounced spikes during major concerts and Vivid Sydney. The radar chart highlights the relative magnitude and duration of these disruptions compared to normal periods.

4 Part 3: Weather Events and Ridership Fluctuations

Rain, Floods, and Recovery

  • Focus: Periods of significant rainfall (e.g., March 2022 floods)

  • Data: BOM and Opal

  • Analysis of train usage during extreme weather events

  • Visualization: Daily entries versus rainfall (dual axis plot)

Key Finding: Severe weather events caused temporary declines in train usage, with recovery typically occurring within days.

# Insert data processing and visualization code here

Inference: Extreme weather events, such as heavy rainfall and flooding, lead to noticeable but brief reductions in train ridership. Unlike the prolonged impact of COVID-19, ridership typically recovers quickly once conditions improve.

5 Appendix

  • Data sources
  • Code snippets
  • Additional visualizations

6 References